Overview

Dataset statistics

Number of variables16
Number of observations157
Missing cells51
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.8 KiB
Average record size in memory128.8 B

Variable types

Categorical4
Numeric12

Alerts

Model has a high cardinality: 156 distinct valuesHigh cardinality
Latest_Launch has a high cardinality: 130 distinct valuesHigh cardinality
four_year_resale_value is highly overall correlated with Price_in_thousands and 6 other fieldsHigh correlation
Price_in_thousands is highly overall correlated with four_year_resale_value and 6 other fieldsHigh correlation
Engine_size is highly overall correlated with four_year_resale_value and 9 other fieldsHigh correlation
Horsepower is highly overall correlated with four_year_resale_value and 8 other fieldsHigh correlation
Wheelbase is highly overall correlated with Engine_size and 5 other fieldsHigh correlation
Width is highly overall correlated with Engine_size and 7 other fieldsHigh correlation
Length is highly overall correlated with Engine_size and 5 other fieldsHigh correlation
Curb_weight is highly overall correlated with four_year_resale_value and 10 other fieldsHigh correlation
Fuel_capacity is highly overall correlated with four_year_resale_value and 10 other fieldsHigh correlation
Fuel_efficiency is highly overall correlated with four_year_resale_value and 9 other fieldsHigh correlation
Power_perf_factor is highly overall correlated with four_year_resale_value and 7 other fieldsHigh correlation
Vehicle_type is highly overall correlated with Curb_weight and 2 other fieldsHigh correlation
four_year_resale_value has 36 (22.9%) missing valuesMissing
Price_in_thousands has 2 (1.3%) missing valuesMissing
Curb_weight has 2 (1.3%) missing valuesMissing
Fuel_efficiency has 3 (1.9%) missing valuesMissing
Power_perf_factor has 2 (1.3%) missing valuesMissing
Model is uniformly distributedUniform
Latest_Launch is uniformly distributedUniform
Sales_in_thousands has unique valuesUnique

Reproduction

Analysis started2023-02-10 07:33:56.793836
Analysis finished2023-02-10 07:34:28.872293
Duration32.08 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

Manufacturer
Categorical

Distinct30
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Dodge
11 
Ford
11 
Toyota
 
9
Chevrolet
 
9
Mercedes-B
 
9
Other values (25)
108 

Length

Max length10
Median length8
Mean length6.7070064
Min length3

Characters and Unicode

Total characters1053
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.3%

Sample

1st rowAcura
2nd rowAcura
3rd rowAcura
4th rowAcura
5th rowAudi

Common Values

ValueCountFrequency (%)
Dodge 11
 
7.0%
Ford 11
 
7.0%
Toyota 9
 
5.7%
Chevrolet 9
 
5.7%
Mercedes-B 9
 
5.7%
Mitsubishi 7
 
4.5%
Nissan 7
 
4.5%
Chrysler 7
 
4.5%
Volvo 6
 
3.8%
Oldsmobile 6
 
3.8%
Other values (20) 75
47.8%

Length

2023-02-10T13:04:29.046094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dodge 11
 
7.0%
ford 11
 
7.0%
toyota 9
 
5.7%
chevrolet 9
 
5.7%
mercedes-b 9
 
5.7%
mitsubishi 7
 
4.5%
nissan 7
 
4.5%
chrysler 7
 
4.5%
volvo 6
 
3.8%
oldsmobile 6
 
3.8%
Other values (20) 75
47.8%

Most occurring characters

ValueCountFrequency (%)
e 96
 
9.1%
o 94
 
8.9%
r 70
 
6.6%
s 65
 
6.2%
a 63
 
6.0%
i 61
 
5.8%
l 57
 
5.4%
d 53
 
5.0%
u 47
 
4.5%
t 41
 
3.9%
Other values (31) 406
38.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 872
82.8%
Uppercase Letter 172
 
16.3%
Dash Punctuation 9
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 96
11.0%
o 94
10.8%
r 70
 
8.0%
s 65
 
7.5%
a 63
 
7.2%
i 61
 
7.0%
l 57
 
6.5%
d 53
 
6.1%
u 47
 
5.4%
t 41
 
4.7%
Other values (13) 225
25.8%
Uppercase Letter
ValueCountFrequency (%)
M 25
14.5%
C 21
12.2%
B 16
9.3%
P 13
 
7.6%
V 12
 
7.0%
D 11
 
6.4%
F 11
 
6.4%
S 9
 
5.2%
T 9
 
5.2%
L 9
 
5.2%
Other values (7) 36
20.9%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1044
99.1%
Common 9
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 96
 
9.2%
o 94
 
9.0%
r 70
 
6.7%
s 65
 
6.2%
a 63
 
6.0%
i 61
 
5.8%
l 57
 
5.5%
d 53
 
5.1%
u 47
 
4.5%
t 41
 
3.9%
Other values (30) 397
38.0%
Common
ValueCountFrequency (%)
- 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 96
 
9.1%
o 94
 
8.9%
r 70
 
6.6%
s 65
 
6.2%
a 63
 
6.0%
i 61
 
5.8%
l 57
 
5.4%
d 53
 
5.0%
u 47
 
4.5%
t 41
 
3.9%
Other values (31) 406
38.6%

Model
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct156
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Neon
 
2
Integra
 
1
Cutlass
 
1
Sentra
 
1
Altima
 
1
Other values (151)
151 

Length

Max length14
Median length12
Mean length6.5541401
Min length2

Characters and Unicode

Total characters1029
Distinct characters60
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique155 ?
Unique (%)98.7%

Sample

1st rowIntegra
2nd rowTL
3rd rowCL
4th rowRL
5th rowA4

Common Values

ValueCountFrequency (%)
Neon 2
 
1.3%
Integra 1
 
0.6%
Cutlass 1
 
0.6%
Sentra 1
 
0.6%
Altima 1
 
0.6%
Maxima 1
 
0.6%
Quest 1
 
0.6%
Pathfinder 1
 
0.6%
Xterra 1
 
0.6%
Frontier 1
 
0.6%
Other values (146) 146
93.0%

Length

2023-02-10T13:04:29.303410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
grand 4
 
2.3%
ram 3
 
1.7%
coupe 3
 
1.7%
town 2
 
1.1%
cherokee 2
 
1.1%
cabrio 2
 
1.1%
montero 2
 
1.1%
carrera 2
 
1.1%
neon 2
 
1.1%
sebring 2
 
1.1%
Other values (153) 153
86.4%

Most occurring characters

ValueCountFrequency (%)
a 96
 
9.3%
r 90
 
8.7%
e 89
 
8.6%
o 60
 
5.8%
n 58
 
5.6%
t 50
 
4.9%
i 48
 
4.7%
C 42
 
4.1%
l 38
 
3.7%
s 33
 
3.2%
Other values (50) 425
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 724
70.4%
Uppercase Letter 212
 
20.6%
Decimal Number 61
 
5.9%
Space Separator 20
 
1.9%
Dash Punctuation 10
 
1.0%
Other Punctuation 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 96
13.3%
r 90
12.4%
e 89
12.3%
o 60
8.3%
n 58
8.0%
t 50
 
6.9%
i 48
 
6.6%
l 38
 
5.2%
s 33
 
4.6%
u 30
 
4.1%
Other values (15) 132
18.2%
Uppercase Letter
ValueCountFrequency (%)
C 42
19.8%
S 32
15.1%
L 17
 
8.0%
M 14
 
6.6%
A 13
 
6.1%
R 10
 
4.7%
V 10
 
4.7%
G 10
 
4.7%
E 9
 
4.2%
T 8
 
3.8%
Other values (13) 47
22.2%
Decimal Number
ValueCountFrequency (%)
0 26
42.6%
3 11
18.0%
4 8
 
13.1%
7 4
 
6.6%
2 4
 
6.6%
8 4
 
6.6%
5 3
 
4.9%
6 1
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
& 1
50.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 936
91.0%
Common 93
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 96
 
10.3%
r 90
 
9.6%
e 89
 
9.5%
o 60
 
6.4%
n 58
 
6.2%
t 50
 
5.3%
i 48
 
5.1%
C 42
 
4.5%
l 38
 
4.1%
s 33
 
3.5%
Other values (38) 332
35.5%
Common
ValueCountFrequency (%)
0 26
28.0%
20
21.5%
3 11
11.8%
- 10
 
10.8%
4 8
 
8.6%
7 4
 
4.3%
2 4
 
4.3%
8 4
 
4.3%
5 3
 
3.2%
. 1
 
1.1%
Other values (2) 2
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 96
 
9.3%
r 90
 
8.7%
e 89
 
8.6%
o 60
 
5.8%
n 58
 
5.6%
t 50
 
4.9%
i 48
 
4.7%
C 42
 
4.1%
l 38
 
3.7%
s 33
 
3.2%
Other values (50) 425
41.3%

Sales_in_thousands
Real number (ℝ)

Distinct157
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.998076
Minimum0.11
Maximum540.561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-02-10T13:04:29.506225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.11
5-th percentile1.8708
Q114.114
median29.45
Q367.956
95-th percentile185.3362
Maximum540.561
Range540.451
Interquartile range (IQR)53.842

Descriptive statistics

Standard deviation68.029422
Coefficient of variation (CV)1.2836206
Kurtosis17.557344
Mean52.998076
Median Absolute Deviation (MAD)20.468
Skewness3.4085184
Sum8320.698
Variance4628.0023
MonotonicityNot monotonic
2023-02-10T13:04:29.738904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.919 1
 
0.6%
1.112 1
 
0.6%
42.643 1
 
0.6%
88.094 1
 
0.6%
79.853 1
 
0.6%
27.308 1
 
0.6%
42.574 1
 
0.6%
54.158 1
 
0.6%
65.005 1
 
0.6%
38.554 1
 
0.6%
Other values (147) 147
93.6%
ValueCountFrequency (%)
0.11 1
0.6%
0.916 1
0.6%
0.954 1
0.6%
1.112 1
0.6%
1.28 1
0.6%
1.38 1
0.6%
1.526 1
0.6%
1.866 1
0.6%
1.872 1
0.6%
3.311 1
0.6%
ValueCountFrequency (%)
540.561 1
0.6%
276.747 1
0.6%
247.994 1
0.6%
245.815 1
0.6%
230.902 1
0.6%
227.061 1
0.6%
220.65 1
0.6%
199.685 1
0.6%
181.749 1
0.6%
175.67 1
0.6%

four_year_resale_value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct117
Distinct (%)96.7%
Missing36
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean18.072975
Minimum5.16
Maximum67.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-02-10T13:04:29.959556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5.16
5-th percentile7.85
Q111.26
median14.18
Q319.875
95-th percentile41.25
Maximum67.55
Range62.39
Interquartile range (IQR)8.615

Descriptive statistics

Standard deviation11.453384
Coefficient of variation (CV)0.63372986
Kurtosis5.7638559
Mean18.072975
Median Absolute Deviation (MAD)3.96
Skewness2.2949155
Sum2186.83
Variance131.18001
MonotonicityNot monotonic
2023-02-10T13:04:30.211038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.75 2
 
1.3%
18.225 2
 
1.3%
16.575 2
 
1.3%
12.025 2
 
1.3%
41.45 1
 
0.6%
20.43 1
 
0.6%
14.795 1
 
0.6%
26.05 1
 
0.6%
58.6 1
 
0.6%
50.375 1
 
0.6%
Other values (107) 107
68.2%
(Missing) 36
 
22.9%
ValueCountFrequency (%)
5.16 1
0.6%
5.86 1
0.6%
7.425 1
0.6%
7.75 2
1.3%
7.825 1
0.6%
7.85 1
0.6%
8.325 1
0.6%
8.45 1
0.6%
8.8 1
0.6%
8.835 1
0.6%
ValueCountFrequency (%)
67.55 1
0.6%
60.625 1
0.6%
58.6 1
0.6%
58.47 1
0.6%
50.375 1
0.6%
41.45 1
0.6%
41.25 1
0.6%
40.375 1
0.6%
39 1
0.6%
36.225 1
0.6%

Vehicle_type
Categorical

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Passenger
116 
Car
41 

Length

Max length9
Median length9
Mean length7.433121
Min length3

Characters and Unicode

Total characters1167
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPassenger
2nd rowPassenger
3rd rowPassenger
4th rowPassenger
5th rowPassenger

Common Values

ValueCountFrequency (%)
Passenger 116
73.9%
Car 41
 
26.1%

Length

2023-02-10T13:04:30.448899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-10T13:04:30.666228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
passenger 116
73.9%
car 41
 
26.1%

Most occurring characters

ValueCountFrequency (%)
s 232
19.9%
e 232
19.9%
a 157
13.5%
r 157
13.5%
P 116
9.9%
n 116
9.9%
g 116
9.9%
C 41
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1010
86.5%
Uppercase Letter 157
 
13.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 232
23.0%
e 232
23.0%
a 157
15.5%
r 157
15.5%
n 116
11.5%
g 116
11.5%
Uppercase Letter
ValueCountFrequency (%)
P 116
73.9%
C 41
 
26.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1167
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 232
19.9%
e 232
19.9%
a 157
13.5%
r 157
13.5%
P 116
9.9%
n 116
9.9%
g 116
9.9%
C 41
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1167
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 232
19.9%
e 232
19.9%
a 157
13.5%
r 157
13.5%
P 116
9.9%
n 116
9.9%
g 116
9.9%
C 41
 
3.5%

Price_in_thousands
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct152
Distinct (%)98.1%
Missing2
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean27.390755
Minimum9.235
Maximum85.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-02-10T13:04:30.862998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9.235
5-th percentile12.469
Q118.0175
median22.799
Q331.9475
95-th percentile55.835
Maximum85.5
Range76.265
Interquartile range (IQR)13.93

Descriptive statistics

Standard deviation14.351653
Coefficient of variation (CV)0.52395975
Kurtosis3.6304123
Mean27.390755
Median Absolute Deviation (MAD)6.099
Skewness1.7657343
Sum4245.567
Variance205.96995
MonotonicityNot monotonic
2023-02-10T13:04:31.369600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.89 2
 
1.3%
38.9 2
 
1.3%
12.64 2
 
1.3%
24.15 1
 
0.6%
20.39 1
 
0.6%
26.249 1
 
0.6%
26.399 1
 
0.6%
29.299 1
 
0.6%
22.799 1
 
0.6%
17.89 1
 
0.6%
Other values (142) 142
90.4%
(Missing) 2
 
1.3%
ValueCountFrequency (%)
9.235 1
0.6%
9.699 1
0.6%
10.685 1
0.6%
11.528 1
0.6%
11.799 1
0.6%
12.05 1
0.6%
12.07 1
0.6%
12.315 1
0.6%
12.535 1
0.6%
12.64 2
1.3%
ValueCountFrequency (%)
85.5 1
0.6%
82.6 1
0.6%
74.97 1
0.6%
71.02 1
0.6%
69.725 1
0.6%
69.7 1
0.6%
62 1
0.6%
60.105 1
0.6%
54.005 1
0.6%
51.728 1
0.6%

Engine_size
Real number (ℝ)

Distinct31
Distinct (%)19.9%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean3.0608974
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-02-10T13:04:31.618066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.8
Q12.3
median3
Q33.575
95-th percentile4.775
Maximum8
Range7
Interquartile range (IQR)1.275

Descriptive statistics

Standard deviation1.044653
Coefficient of variation (CV)0.34128977
Kurtosis2.344782
Mean3.0608974
Median Absolute Deviation (MAD)0.7
Skewness1.1004473
Sum477.5
Variance1.0912998
MonotonicityNot monotonic
2023-02-10T13:04:31.813519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2 17
 
10.8%
3 14
 
8.9%
2.4 11
 
7.0%
2.5 11
 
7.0%
4.6 9
 
5.7%
1.8 8
 
5.1%
3.5 8
 
5.1%
3.8 8
 
5.1%
4 7
 
4.5%
3.4 7
 
4.5%
Other values (21) 56
35.7%
ValueCountFrequency (%)
1 1
 
0.6%
1.5 1
 
0.6%
1.6 1
 
0.6%
1.8 8
5.1%
1.9 5
 
3.2%
2 17
10.8%
2.2 4
 
2.5%
2.3 6
 
3.8%
2.4 11
7.0%
2.5 11
7.0%
ValueCountFrequency (%)
8 1
 
0.6%
5.7 2
 
1.3%
5.4 1
 
0.6%
5.2 2
 
1.3%
5 2
 
1.3%
4.7 2
 
1.3%
4.6 9
5.7%
4.3 2
 
1.3%
4.2 1
 
0.6%
4 7
4.5%

Horsepower
Real number (ℝ)

Distinct66
Distinct (%)42.3%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean185.94872
Minimum55
Maximum450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-02-10T13:04:32.041797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile114.5
Q1149.5
median177.5
Q3215
95-th percentile300
Maximum450
Range395
Interquartile range (IQR)65.5

Descriptive statistics

Standard deviation56.700321
Coefficient of variation (CV)0.30492451
Kurtosis2.4066575
Mean185.94872
Median Absolute Deviation (MAD)32.5
Skewness1.000695
Sum29008
Variance3214.9264
MonotonicityNot monotonic
2023-02-10T13:04:32.273329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 9
 
5.7%
170 9
 
5.7%
200 8
 
5.1%
210 7
 
4.5%
115 6
 
3.8%
185 5
 
3.2%
175 5
 
3.2%
275 5
 
3.2%
120 4
 
2.5%
190 4
 
2.5%
Other values (56) 94
59.9%
ValueCountFrequency (%)
55 1
 
0.6%
92 1
 
0.6%
100 2
 
1.3%
106 1
 
0.6%
107 1
 
0.6%
110 1
 
0.6%
113 1
 
0.6%
115 6
3.8%
119 1
 
0.6%
120 4
2.5%
ValueCountFrequency (%)
450 1
 
0.6%
345 1
 
0.6%
310 1
 
0.6%
302 2
 
1.3%
300 4
2.5%
290 1
 
0.6%
275 5
3.2%
255 1
 
0.6%
253 3
1.9%
250 1
 
0.6%

Wheelbase
Real number (ℝ)

Distinct88
Distinct (%)56.4%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean107.48718
Minimum92.6
Maximum138.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-02-10T13:04:32.530939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum92.6
5-th percentile95.875
Q1103
median107
Q3112.2
95-th percentile119.25
Maximum138.7
Range46.1
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation7.641303
Coefficient of variation (CV)0.071090367
Kurtosis2.8592849
Mean107.48718
Median Absolute Deviation (MAD)4.6
Skewness0.96993566
Sum16768
Variance58.389512
MonotonicityNot monotonic
2023-02-10T13:04:32.754487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112.2 8
 
5.1%
107 5
 
3.2%
113 5
 
3.2%
102.4 4
 
2.5%
109 4
 
2.5%
106.5 4
 
2.5%
108 4
 
2.5%
98.9 4
 
2.5%
106.4 4
 
2.5%
107.3 4
 
2.5%
Other values (78) 110
70.1%
ValueCountFrequency (%)
92.6 2
1.3%
93.1 1
0.6%
93.4 1
0.6%
94.5 2
1.3%
94.9 1
0.6%
95.2 1
0.6%
96.1 1
0.6%
96.2 1
0.6%
97 1
0.6%
97.1 1
0.6%
ValueCountFrequency (%)
138.7 1
0.6%
138.5 1
0.6%
131 1
0.6%
127.2 1
0.6%
121.5 1
0.6%
120.7 1
0.6%
120 2
1.3%
119 2
1.3%
118.1 1
0.6%
117.7 1
0.6%

Width
Real number (ℝ)

Distinct78
Distinct (%)50.0%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean71.15
Minimum62.6
Maximum79.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-02-10T13:04:32.986664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum62.6
5-th percentile66.5
Q168.4
median70.55
Q373.425
95-th percentile78.2
Maximum79.9
Range17.3
Interquartile range (IQR)5.025

Descriptive statistics

Standard deviation3.4518719
Coefficient of variation (CV)0.048515416
Kurtosis-0.30046753
Mean71.15
Median Absolute Deviation (MAD)2.4
Skewness0.48386207
Sum11099.4
Variance11.915419
MonotonicityNot monotonic
2023-02-10T13:04:33.219189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.7 6
 
3.8%
74.4 6
 
3.8%
68.3 5
 
3.2%
70.3 5
 
3.2%
72.7 5
 
3.2%
69.1 4
 
2.5%
69.4 4
 
2.5%
66.5 4
 
2.5%
67.5 3
 
1.9%
73.6 3
 
1.9%
Other values (68) 111
70.7%
ValueCountFrequency (%)
62.6 1
 
0.6%
65.7 1
 
0.6%
66.4 3
1.9%
66.5 4
2.5%
66.7 6
3.8%
66.9 2
 
1.3%
67 1
 
0.6%
67.1 1
 
0.6%
67.3 2
 
1.3%
67.4 1
 
0.6%
ValueCountFrequency (%)
79.9 1
 
0.6%
79.3 1
 
0.6%
79.1 1
 
0.6%
78.8 2
1.3%
78.7 1
 
0.6%
78.2 3
1.9%
77 1
 
0.6%
76.8 2
1.3%
76.6 1
 
0.6%
76.4 2
1.3%

Length
Real number (ℝ)

Distinct127
Distinct (%)81.4%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean187.34359
Minimum149.4
Maximum224.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-02-10T13:04:33.460217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum149.4
5-th percentile163.675
Q1177.575
median187.9
Q3196.125
95-th percentile208.5
Maximum224.5
Range75.1
Interquartile range (IQR)18.55

Descriptive statistics

Standard deviation13.431754
Coefficient of variation (CV)0.071695831
Kurtosis0.30257402
Mean187.34359
Median Absolute Deviation (MAD)9.4
Skewness-0.059046823
Sum29225.6
Variance180.41202
MonotonicityNot monotonic
2023-02-10T13:04:33.684314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
186.3 4
 
2.5%
189.2 3
 
1.9%
192 3
 
1.9%
190.4 3
 
1.9%
163.3 2
 
1.3%
192.5 2
 
1.3%
174 2
 
1.3%
212 2
 
1.3%
193.5 2
 
1.3%
208.5 2
 
1.3%
Other values (117) 131
83.4%
ValueCountFrequency (%)
149.4 1
0.6%
152 1
0.6%
157.3 1
0.6%
157.9 1
0.6%
160.4 1
0.6%
161.1 1
0.6%
163.3 2
1.3%
163.8 1
0.6%
165.4 1
0.6%
166.7 1
0.6%
ValueCountFrequency (%)
224.5 1
0.6%
224.2 1
0.6%
215.3 1
0.6%
215 1
0.6%
212 2
1.3%
209.1 1
0.6%
208.5 2
1.3%
207.7 1
0.6%
207.2 1
0.6%
206.8 1
0.6%

Curb_weight
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct147
Distinct (%)94.8%
Missing2
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean3.3780258
Minimum1.895
Maximum5.572
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-02-10T13:04:33.940288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.895
5-th percentile2.4235
Q12.971
median3.342
Q33.7995
95-th percentile4.3891
Maximum5.572
Range3.677
Interquartile range (IQR)0.8285

Descriptive statistics

Standard deviation0.63050163
Coefficient of variation (CV)0.18664796
Kurtosis1.2654536
Mean3.3780258
Median Absolute Deviation (MAD)0.41
Skewness0.70815824
Sum523.594
Variance0.39753231
MonotonicityNot monotonic
2023-02-10T13:04:34.151969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.769 3
 
1.9%
2.998 3
 
1.9%
2.91 2
 
1.3%
3.075 2
 
1.3%
3.876 2
 
1.3%
3.368 2
 
1.3%
3.455 1
 
0.6%
2.958 1
 
0.6%
3.102 1
 
0.6%
4.387 1
 
0.6%
Other values (137) 137
87.3%
(Missing) 2
 
1.3%
ValueCountFrequency (%)
1.895 1
0.6%
2.24 1
0.6%
2.25 1
0.6%
2.332 1
0.6%
2.339 1
0.6%
2.367 1
0.6%
2.398 1
0.6%
2.42 1
0.6%
2.425 1
0.6%
2.452 1
0.6%
ValueCountFrequency (%)
5.572 1
0.6%
5.401 1
0.6%
5.393 1
0.6%
5.115 1
0.6%
4.808 1
0.6%
4.52 1
0.6%
4.47 1
0.6%
4.394 1
0.6%
4.387 1
0.6%
4.298 1
0.6%

Fuel_capacity
Real number (ℝ)

Distinct55
Distinct (%)35.3%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean17.951923
Minimum10.3
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-02-10T13:04:34.403680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10.3
5-th percentile12.5
Q115.8
median17.2
Q319.575
95-th percentile25.4
Maximum32
Range21.7
Interquartile range (IQR)3.775

Descriptive statistics

Standard deviation3.8879213
Coefficient of variation (CV)0.21657408
Kurtosis2.0728132
Mean17.951923
Median Absolute Deviation (MAD)1.9
Skewness1.1367124
Sum2800.5
Variance15.115932
MonotonicityNot monotonic
2023-02-10T13:04:34.656871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.5 14
 
8.9%
17 9
 
5.7%
20 8
 
5.1%
19 8
 
5.1%
16 7
 
4.5%
13.2 6
 
3.8%
15.9 6
 
3.8%
15 5
 
3.2%
14.5 5
 
3.2%
17.5 5
 
3.2%
Other values (45) 83
52.9%
ValueCountFrequency (%)
10.3 1
 
0.6%
11.9 2
 
1.3%
12 1
 
0.6%
12.1 3
1.9%
12.5 2
 
1.3%
12.7 1
 
0.6%
13.1 2
 
1.3%
13.2 6
3.8%
13.7 1
 
0.6%
14 1
 
0.6%
ValueCountFrequency (%)
32 2
1.3%
30 2
1.3%
26 3
1.9%
25.4 2
1.3%
25.1 1
 
0.6%
25 3
1.9%
24.3 1
 
0.6%
23.7 1
 
0.6%
23.2 2
1.3%
22.5 1
 
0.6%

Fuel_efficiency
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)13.0%
Missing3
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean23.844156
Minimum15
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-02-10T13:04:34.880707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile16.65
Q121
median24
Q326
95-th percentile31
Maximum45
Range30
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.2827056
Coefficient of variation (CV)0.17961238
Kurtosis3.2411308
Mean23.844156
Median Absolute Deviation (MAD)2
Skewness0.69232776
Sum3672
Variance18.341567
MonotonicityNot monotonic
2023-02-10T13:04:35.064551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
25 23
14.6%
24 16
10.2%
27 15
9.6%
22 14
8.9%
21 14
8.9%
23 14
8.9%
26 12
 
7.6%
19 6
 
3.8%
20 5
 
3.2%
15 5
 
3.2%
Other values (10) 30
19.1%
ValueCountFrequency (%)
15 5
 
3.2%
16 3
 
1.9%
17 3
 
1.9%
18 5
 
3.2%
19 6
 
3.8%
20 5
 
3.2%
21 14
8.9%
22 14
8.9%
23 14
8.9%
24 16
10.2%
ValueCountFrequency (%)
45 1
 
0.6%
33 4
 
2.5%
32 1
 
0.6%
31 3
 
1.9%
30 5
 
3.2%
29 2
 
1.3%
28 3
 
1.9%
27 15
9.6%
26 12
7.6%
25 23
14.6%

Latest_Launch
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct130
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
10/5/2012
 
2
1/24/2011
 
2
4/26/2011
 
2
6/25/2011
 
2
9/21/2011
 
2
Other values (125)
147 

Length

Max length10
Median length9
Mean length8.9681529
Min length8

Characters and Unicode

Total characters1408
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)65.6%

Sample

1st row2/2/2012
2nd row6/3/2011
3rd row1/4/2012
4th row3/10/2011
5th row10/8/2011

Common Values

ValueCountFrequency (%)
10/5/2012 2
 
1.3%
1/24/2011 2
 
1.3%
4/26/2011 2
 
1.3%
6/25/2011 2
 
1.3%
9/21/2011 2
 
1.3%
2/18/2011 2
 
1.3%
4/1/2011 2
 
1.3%
5/31/2011 2
 
1.3%
8/27/2011 2
 
1.3%
9/25/2011 2
 
1.3%
Other values (120) 137
87.3%

Length

2023-02-10T13:04:35.259521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10/5/2012 2
 
1.3%
8/31/2011 2
 
1.3%
1/24/2011 2
 
1.3%
10/30/2012 2
 
1.3%
2/23/2012 2
 
1.3%
11/24/2012 2
 
1.3%
8/16/2012 2
 
1.3%
1/4/2012 2
 
1.3%
9/10/2012 2
 
1.3%
4/24/2011 2
 
1.3%
Other values (120) 137
87.3%

Most occurring characters

ValueCountFrequency (%)
1 365
25.9%
2 325
23.1%
/ 314
22.3%
0 190
13.5%
3 36
 
2.6%
4 34
 
2.4%
6 33
 
2.3%
8 30
 
2.1%
9 28
 
2.0%
7 27
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1094
77.7%
Other Punctuation 314
 
22.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 365
33.4%
2 325
29.7%
0 190
17.4%
3 36
 
3.3%
4 34
 
3.1%
6 33
 
3.0%
8 30
 
2.7%
9 28
 
2.6%
7 27
 
2.5%
5 26
 
2.4%
Other Punctuation
ValueCountFrequency (%)
/ 314
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1408
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 365
25.9%
2 325
23.1%
/ 314
22.3%
0 190
13.5%
3 36
 
2.6%
4 34
 
2.4%
6 33
 
2.3%
8 30
 
2.1%
9 28
 
2.0%
7 27
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 365
25.9%
2 325
23.1%
/ 314
22.3%
0 190
13.5%
3 36
 
2.6%
4 34
 
2.4%
6 33
 
2.3%
8 30
 
2.1%
9 28
 
2.0%
7 27
 
1.9%

Power_perf_factor
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct154
Distinct (%)99.4%
Missing2
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean77.043591
Minimum23.276272
Maximum188.14432
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-02-10T13:04:35.467307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum23.276272
5-th percentile46.203997
Q160.407707
median72.030917
Q389.414878
95-th percentile125.09151
Maximum188.14432
Range164.86805
Interquartile range (IQR)29.007171

Descriptive statistics

Standard deviation25.142664
Coefficient of variation (CV)0.32634336
Kurtosis2.0812929
Mean77.043591
Median Absolute Deviation (MAD)14.241606
Skewness1.070635
Sum11941.757
Variance632.15356
MonotonicityNot monotonic
2023-02-10T13:04:35.699924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.08489875 2
 
1.3%
58.28014952 1
 
0.6%
86.27252291 1
 
0.6%
63.31372783 1
 
0.6%
89.42782031 1
 
0.6%
71.17166413 1
 
0.6%
72.29035508 1
 
0.6%
69.78294434 1
 
0.6%
67.88927059 1
 
0.6%
60.86161155 1
 
0.6%
Other values (144) 144
91.7%
(Missing) 2
 
1.3%
ValueCountFrequency (%)
23.27627233 1
0.6%
36.67228358 1
0.6%
39.98642475 1
0.6%
40.70007242 1
0.6%
42.87909734 1
0.6%
43.11713201 1
0.6%
44.08370946 1
0.6%
45.83218056 1
0.6%
46.36334747 1
0.6%
46.94387676 1
0.6%
ValueCountFrequency (%)
188.144323 1
0.6%
141.14115 1
0.6%
141.1009845 1
0.6%
139.9822936 1
0.6%
135.9147096 1
0.6%
134.6568582 1
0.6%
134.3909754 1
0.6%
125.2738757 1
0.6%
125.0133574 1
0.6%
124.4467163 1
0.6%

Interactions

2023-02-10T13:04:25.267806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:03:58.051949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:00.406737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:03.298917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:06.554612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:08.885509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:11.405992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:13.767143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:16.296911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:18.407036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:20.720567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:22.814856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:25.453255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:03:58.275878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:00.589619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:03.483074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:06.771899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:09.054789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:11.557230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:13.945513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:16.481797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:18.579967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:20.880259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:22.984159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:25.620521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:03:58.472791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:00.770759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:03.672741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:06.948094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:09.255257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:11.738345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:14.114785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:16.644533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:18.749280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:21.041157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:23.172579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:25.805114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:03:58.697911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:00.951126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:03.864456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:07.130212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:09.556295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:11.898032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:14.306109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:16.829012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:18.934154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:21.208498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:23.356217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:25.974349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:03:58.904319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:01.175432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:04.054331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:07.329678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:09.783073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:12.341156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:14.535990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:17.013487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:19.111718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:21.400756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:23.533033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:26.174838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:03:59.130325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:01.432718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:04.834359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:07.532156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:09.991484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:12.503958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:14.803104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:17.198423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:19.300651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:21.598227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:23.717936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:26.337376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:03:59.288859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:01.670397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:05.023067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:07.697796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:10.153882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:12.664781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:15.045679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:17.361053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:19.462305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:21.777879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:23.886725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:26.515871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:03:59.485954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:01.858435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:05.288283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:07.891528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:10.338872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:12.842043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:15.320745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:17.545893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:19.644011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:21.972386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:24.106577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:26.678643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:03:59.674236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:02.037265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:05.514841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:08.106462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:10.531941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:13.093017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:15.529943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:17.715075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:19.835384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:22.160853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:24.405835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:26.863161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:03:59.835658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:02.324957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:05.830624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:08.307447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:10.724313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:13.297502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:15.780074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:17.900048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:20.004693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:22.329346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:24.667344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:27.016812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:00.038234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:02.641108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:06.060889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:08.490108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:10.893617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:13.447724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:15.942742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:18.062682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:20.174236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:22.491192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:24.868534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:27.195231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:00.234711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:03.022089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:06.356044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:08.669124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:11.086412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:13.609452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:16.127644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:18.237266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:20.352690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:22.655513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-02-10T13:04:25.067041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-02-10T13:04:35.910370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Sales_in_thousandsfour_year_resale_valuePrice_in_thousandsEngine_sizeHorsepowerWheelbaseWidthLengthCurb_weightFuel_capacityFuel_efficiencyPower_perf_factorManufacturerVehicle_type
Sales_in_thousands1.000-0.448-0.492-0.082-0.3200.1600.0170.139-0.064-0.0440.133-0.3570.0000.168
four_year_resale_value-0.4481.0000.9180.6030.7700.2420.3360.2360.6190.607-0.5690.8080.2990.243
Price_in_thousands-0.4920.9181.0000.6670.8870.3370.4540.3050.6820.596-0.6130.9260.3060.134
Engine_size-0.0820.6030.6671.0000.8330.6080.7200.6410.8290.741-0.7840.8150.2830.321
Horsepower-0.3200.7700.8870.8331.0000.4860.6160.5010.7340.635-0.6570.9940.2470.000
Wheelbase0.1600.2420.3370.6080.4861.0000.7100.8520.7280.660-0.5100.4650.0000.459
Width0.0170.3360.4540.7200.6160.7101.0000.7160.7210.635-0.5620.5960.1570.387
Length0.1390.2360.3050.6410.5010.8520.7161.0000.7010.599-0.4390.4670.1810.000
Curb_weight-0.0640.6190.6820.8290.7340.7280.7210.7011.0000.876-0.7970.7340.1290.520
Fuel_capacity-0.0440.6070.5960.7410.6350.6600.6350.5990.8761.000-0.8230.6370.2140.647
Fuel_efficiency0.133-0.569-0.613-0.784-0.657-0.510-0.562-0.439-0.797-0.8231.000-0.6600.2070.640
Power_perf_factor-0.3570.8080.9260.8150.9940.4650.5960.4670.7340.637-0.6601.0000.2940.000
Manufacturer0.0000.2990.3060.2830.2470.0000.1570.1810.1290.2140.2070.2941.0000.333
Vehicle_type0.1680.2430.1340.3210.0000.4590.3870.0000.5200.6470.6400.0000.3331.000

Missing values

2023-02-10T13:04:27.495294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-10T13:04:27.919223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-10T13:04:28.401263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ManufacturerModelSales_in_thousandsfour_year_resale_valueVehicle_typePrice_in_thousandsEngine_sizeHorsepowerWheelbaseWidthLengthCurb_weightFuel_capacityFuel_efficiencyLatest_LaunchPower_perf_factor
0AcuraIntegra16.91916.360Passenger21.501.8140.0101.267.3172.42.63913.228.02/2/201258.280150
1AcuraTL39.38419.875Passenger28.403.2225.0108.170.3192.93.51717.225.06/3/201191.370778
2AcuraCL14.11418.225PassengerNaN3.2225.0106.970.6192.03.47017.226.01/4/2012NaN
3AcuraRL8.58829.725Passenger42.003.5210.0114.671.4196.63.85018.022.03/10/201191.389779
4AudiA420.39722.255Passenger23.991.8150.0102.668.2178.02.99816.427.010/8/201162.777639
5AudiA618.78023.555Passenger33.952.8200.0108.776.1192.03.56118.522.08/9/201184.565105
6AudiA81.38039.000Passenger62.004.2310.0113.074.0198.23.90223.721.02/27/2012134.656858
7BMW323i19.747NaNPassenger26.992.5170.0107.368.4176.03.17916.626.06/28/201171.191207
8BMW328i9.23128.675Passenger33.402.8193.0107.368.5176.03.19716.624.01/29/201281.877069
9BMW528i17.52736.125Passenger38.902.8193.0111.470.9188.03.47218.525.04/4/201183.998724
ManufacturerModelSales_in_thousandsfour_year_resale_valueVehicle_typePrice_in_thousandsEngine_sizeHorsepowerWheelbaseWidthLengthCurb_weightFuel_capacityFuel_efficiencyLatest_LaunchPower_perf_factor
147VolkswagenPassat51.10216.725Passenger21.201.8150.0106.468.5184.13.04316.427.010/30/201261.701381
148VolkswagenCabrio9.56916.575Passenger19.992.0115.097.466.7160.43.07913.726.05/31/201148.907372
149VolkswagenGTI5.59613.760Passenger17.502.0115.098.968.3163.32.76214.626.04/1/201147.946841
150VolkswagenBeetle49.463NaNPassenger15.902.0115.098.967.9161.12.76914.526.010/20/201147.329632
151VolvoS4016.957NaNPassenger23.401.9160.0100.567.6176.62.99815.825.02/18/201166.113057
152VolvoV403.545NaNPassenger24.401.9160.0100.567.6176.63.04215.825.09/21/201166.498812
153VolvoS7015.245NaNPassenger27.502.4168.0104.969.3185.93.20817.925.011/24/201270.654495
154VolvoV7017.531NaNPassenger28.802.4168.0104.969.3186.23.25917.925.06/25/201171.155978
155VolvoC703.493NaNPassenger45.502.3236.0104.971.5185.73.60118.523.04/26/2011101.623357
156VolvoS8018.969NaNPassenger36.002.9201.0109.972.1189.83.60021.124.011/14/201185.735655